Advances in analytical instruments, examples of which can include microscopes, spectrometers, and diffractometers, have dramatically improved the quantity and the quality of data obtainable by the instruments. For example, the spatial resolutions that can be achieved in specimen micrographs today are much more refined than that of the past. However, practically speaking, the quantity and quality of information obtainable is now often limited by observer effects on the specimen and/or acquisition times that are too long.
Application of computational imaging techniques to reconstruct representations of fully-sampled information from sparse datasets obtained by sub-sampling a specimen can theoretically minimize observer effects and acquisition times. However, operational limitations of the analytical instruments can prevent successful sub-sampling, which can make implementation of such computational imaging techniques difficult and/or impossible. Accordingly, there exists a need for sub-sampling and associated computational imaging techniques that minimize observer effects and acquisition times.